• Laser & Optoelectronics Progress
  • Vol. 61, Issue 2, 0211007 (2024)
Zhengjun Liu1、**, Xuyang Zhou1、*, Xiu Wen2, Yutong Li1, and Shutian Liu1
Author Affiliations
  • 1School of Physics, Harbin Institute of Technology, Harbin 150001, Heilongjiang , China
  • 2School of Physical Science and Technology, Tiangong University, Tianjin 300387, China
  • show less
    DOI: 10.3788/LOP232366 Cite this Article Set citation alerts
    Zhengjun Liu, Xuyang Zhou, Xiu Wen, Yutong Li, Shutian Liu. Review of Methods for Enhancing Measurement and Computation Speeds in Computational Optical Imaging Systems (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(2): 0211007 Copy Citation Text show less

    Abstract

    To overcome the disadvantages of slow measurement and computation in scanning computational imaging systems, this paper summarizes several rapid computational imaging techniques and introduces methods for enhancing measurement and computation speeds. It delves into computational optical imaging methods based on light field modulation, highlighting various approaches such as axial scanning, transverse scanning, multiwavelength scanning, scattering media, and multidistance techniques. Furthermore, it explores fast quantitative phase imaging techniques, including the standard quantitative phase imaging method, rapid variant based on the Kramers-Kronig relation, computational imaging method using diagonal spread sampling, and single-frame computational imaging method employing symmetrical illumination. Additionally, it covers autofocus technologies, detailing the classification of autofocus technology, its core algorithms, the autofocus method based on the Tanimoto coefficient and the absolute value of the polyphase gradient, and the rapid autofocus method based on feature region extraction and subdivision search.
    Zhengjun Liu, Xuyang Zhou, Xiu Wen, Yutong Li, Shutian Liu. Review of Methods for Enhancing Measurement and Computation Speeds in Computational Optical Imaging Systems (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(2): 0211007
    Download Citation